With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit internal user information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields inside one event have proven to be strong indicators for fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in the risk management system of one of the world's largest e-commerce platforms, which utilize it to provide real-time transaction fraud detection. Experimental results on real industrial data from different regions in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.
Centre for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology（人工智能与机器学习中心，银行技术发展与研究院）
Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chao…
Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions
University of Stirling（斯特灵大学）；University of Nottingham（诺丁汉大学）
Fraud detection is a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Thus, fraud detection with the aid of smart versions of machine learning (ML) tools is essential to assure safety. Fraud detection is a primary ML classification task; however, the optimum performance of the corresponding ML tool relies on the usage of the best hyperparameter values. Moreover, classification under imbalanced classes is quite challenging as it causes poor performance in minority classes, which most ML c…
QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
Department of Physics, Indian Institute of Science Education and Research（印度科学教育与研究学院物理系）；Bikash’s Quantum (OPC) Pvt. Ltd.（比克ash量子（OPC）私人有限公司）；Nottingham Trent University（诺丁汉特伦特大学）；Qatar Center for Quantum Computing, College of Science and Engineering（卡塔尔量子计算中心，科学与工程学院）；Hamad Bin Khalifa University（哈马德·本·卡西姆大学）；Department of Computer Science, Faculty of Computers and Artificial Intelligence（计算机科学系，计算机与人工智能学院）
KU Leuven（鲁汶大学）；Applied Statistics GmbH（应用统计有限公司）；University of Antwerp（安特卫普大学）
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, fraud occurs in typically less than 0.5% of the cases. Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class. A frequent problem when analyzing real data is the presence of anom…
Ant Financial Services Group（蚂蚁金融服务集团）；Tsinghua University（清华大学）
With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also pos…
Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations
School of Science；Engineering, Al Akhawayn University, Ifrane, Morocco.；Engineering Sciences Laboratory,Polydisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；Signal Systems \& Components Laboratory, Faculty of Sciences；Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；University of Medina Saudi Arabia, Kingdom of Saudi Arabia
Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection
Qkrishi Quantum India（Qkrishi量子印度）；Theoretical Sciences Unit Jawaharlal Nehru Centre For Advanced Scientific Research（贾瓦哈拉尔·奈尔中心高级科学研究所理论科学单元）；Dept. of ECE NC State University（电子与计算机工程系北卡罗来纳州立大学）
RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Institute of FinTech, Fudan University（复旦大学金融科技研究院）；Shanghai ChinaPay e-payment Service Co.,Ltd（上海银联电子支付服务有限公司）
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE（纽约大学阿布扎比分校（NYUAD）工程学院 eBRAIN 实验室，阿布扎赫，阿联酋）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE（量子与拓扑系统中心（CQTS），NYUAD 研究院，纽约大学阿布扎比分校（NYUAD），阿布扎赫，阿联酋）
Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
University of Rochester（罗切斯特大学）；University of Minnesota（明尼苏达大学）；Northeastern University（东北大学）；University of California（加利福尼亚大学）；Cornell University（康奈尔大学）
Bank transaction fraud results in over $13B annual losses for banks, merchants, and card holders worldwide. Much of this fraud starts with a Point-of-Compromise (a data breach or a skimming operation) where credit and debit card digital information is stolen, resold, and later used to perform fraud. We introduce this problem and present an automatic Points-of-Compromise (POC) detection procedure. BreachRadar is a distributed alternating algorithm that assigns a probability of being compromised to the different possible locations. We implement this method using Apache Spark and show its linear …
Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud detection has been gaining momentum. However, most existing methods are based on the strong inductive bias of homophily, which indicates that the context neighbors tend to have same labels or similar features. In real scenarios, fraudsters often engage in camouflage behaviors in order to avoid detection system. Therefore, the homophilic assumption no longer holds, which is known as the inconsistency problem. In this pap…
University of Colorado at Boulder（科罗拉多大学博尔德分校）；University of Maryland, College Park（马里兰大学帕克分校）；IUPUI（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithm…
CNRS（法国国家科学研究中心）；Université Paris-Saclay（巴黎-萨克雷大学）；ESSEC Business School（埃塞克高等商学院）；CREST（经济与统计研究中心）；ENSAE（国立统计与经济管理学院）；Université d’Évry Val d’Essonne（埃夫里-瓦尔德埃松大学）
Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine failures. Identifying outliers in the presence of missing links is therefore a crucial problem in network analysis. In this work, we introduce a new algorithm to detect outliers in a network that simultaneously predicts the missing links. The proposed method is statistically sound: …
In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements. However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior. For example, search engine traffic for florists usually tends to peak around Valentine's day, Mother's day, etc. To overcome, this challenge, in this manuscript we propose three models which are able to incorporate the effects arising due to variations in product demand. The proposed models are a combination of …
University of Electro-Communications（电气通信大学）；Waseda University（早稻田大学）
In audio applications, one of the most important representations of audio signals is the amplitude spectrogram. It is utilized in many machine-learning-based information processing methods including the ones using the restricted Boltzmann machines (RBM). However, the ordinary Gaussian-Bernoulli RBM (the most popular RBM among its variations) cannot directly handle amplitude spectra because the Gaussian distribution is a symmetric model allowing negative values which never appear in the amplitude. In this paper, after proposing a general gamma Boltzmann machine, we propose a practical model cal…
